{"id":"W4319990891","doi":"10.18280/ts.390625","title":"A Hybrid Model: Multiple Feature Selection Approach Using Transfer Learning for Bacteria Classification","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature selection; Artificial intelligence; Pattern recognition (psychology); Computer science; Support vector machine; Transfer of learning; Feature (linguistics); Feature extraction; Selection (genetic algorithm); Contextual image classification; Machine learning; Data mining; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001476995,0.001342878,0.001432167,0.001654784,0.0004866954,0.0009831997,0.001539799,0.001279493,0.001535918],"category_scores_gemma":[0.001635958,0.0003410093,0.001817662,0.001179307,0.0003702666,0.001177342,0.0009523156,0.0009841088,0.0008317505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000643481,"about_ca_system_score_gemma":0.0007866556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005350935,"about_ca_topic_score_gemma":0.003270226,"domain_scores_codex":[0.9993157,0.0001582614,0.00004383446,0.0001691958,0.000204189,0.0001088325],"domain_scores_gemma":[0.9994393,0.000206666,0.00004494595,0.00003982073,0.0002405968,0.00002874741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000676655,0.0007317621,0.007415794,0.0001847592,0.0005103128,0.0003181075,0.0001518754,0.3393313,0.01931946,0.001374769,0.004662503,0.6253226],"study_design_scores_gemma":[0.000009749903,0.0001235711,0.000733589,0.000005589503,0.00002827962,0.00004896777,0.00001302927,0.9964716,0.001706862,0.0004594872,0.000386752,0.00001248444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.113687,0.001348548,0.8792018,0.0004641476,0.0002022541,0.0002725907,0.0002795019,0.002722902,0.001821162],"genre_scores_gemma":[0.8752745,0.0005342004,0.1173207,0.0003130228,0.000141164,0.0005081097,0.0008016942,0.0001089241,0.004997702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005350935,"threshold_uncertainty_score":0.01063961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03313844618207765,"score_gpt":0.2455867326385171,"score_spread":0.2124482864564395,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}